Effects of AST-120 on mortality in patients with chronic kidney disease modeled by artificial intelligence or traditional statistical analysis

Chronic kidney disease (CKD) imposes a substantial burden, and patient prognosis remains grim. The impact of AST-120 (AST-120) on the survival of CKD patients lacks a consensus. This study aims to investigate the effects of AST-120 usage on the survival of CKD patients and explore the utility of artificial intelligence models for decision-making. We conducted a retrospective analysis of CKD patients receiving care in the pre-end-stage renal disease (ESRD) program at Taichung Veterans General Hospital from 2000 to 2019. We employed Cox regression models to evaluate the relationship between AST-120 use and patient survival, both before and after propensity score matching. Subsequently, we employed Deep Neural Network (DNN) and Extreme Gradient Boosting (XGBoost) models to assess their performance in predicting AST-120's impact on patient survival. Among the 2584 patients in our cohort, 2199 did not use AST-120, while 385 patients received AST-120. AST-120 users exhibited significantly lower mortality rates compared to non-AST-120 users (13.51% vs. 37.88%, p < 0.0001) and a reduced prevalence of ESRD (44.16% vs. 53.17%, p = 0.0005). Propensity score matching at 1:1 and 1:2 revealed no significant differences, except for dialysis and all-cause mortality, where AST-120 users exhibited significantly lower all-cause mortality (p < 0.0001), with a hazard ratio (HR) of 0.395 (95% CI = 0.295–0.522). This difference remained statistically significant even after propensity matching. In terms of model performance, the XGBoost model demonstrated the highest accuracy (0.72), specificity (0.90), and positive predictive value (0.48), while the logistic regression model showed the highest sensitivity (0.63) and negative predictive value (0.84). The area under the curve (AUC) values for logistic regression, DNN, and XGBoost were 0.73, 0.73, and 0.69, respectively, indicating similar predictive capabilities for mortality. In this cohort of CKD patients, the use of AST-120 is significantly associated with reduced mortality. However, the performance of artificial intelligence models in predicting the impact of AST-120 is not superior to statistical analysis using the current architecture and algorithm.

The progression from any renal disease unresolved within 3 months ultimately leads to chronic kidney disease (CKD).CKD encompasses a diverse array of disorders characterized by both structural and functional impairment of the kidneys.These manifestations vary widely, with outcomes influenced by underlying causes and disease severity 1,2 .Recent years have seen numerous mechanisms proposed to elucidate the progression of CKD, and the global burden of disease report in 2017 revealed CKD as a significant contributor to mortality, causing 1.2 million deaths and ranking as the 12th leading cause of death worldwide 3 .All-age CKD mortality increased by 41.5% from 1990 to 2017 3 .A recent analysis estimated the global prevalence of CKD at 9.1% (697.5 million

Study design
Our study was conducted at Taichung Veterans General Hospital (TCVGH) from January 1, 2000, to December 31, 2019, and focused on patients enrolled in our pre-end-stage renal disease (pre-ESRD) program.Notably, our pre-ESRD pay-for-performance (P4P) care program has a strong track record of excellent patient compliance with medication and follow-up periods.The primary objectives of our research were to investigate the impact of AST-120 on patient survival and to develop a predictive model for determining the optimal use of AST-120.
To achieve these objectives, we employed Cox regression analysis, which included both univariate and multivariate analyses, to explore any potential associations between AST-120 usage and patient survival.We also implemented propensity matching to minimize potential confounding factors.The differences in survival between AST-120 users and non-users were assessed through Kaplan-Meier survival curves.
In the event that we identify a significant relationship between patient survival and AST-120 usage, our next step will involve leveraging AI to construct a predictive model with enhanced predictive capabilities.This model aims to assist healthcare professionals in making well-informed decisions regarding the utilization of AST-120 as a treatment option.If the AI model, designed to predict AST-120 usage associated with improved patient survival, outperforms a conventional logistic regression model in terms of predictive power, we will use the Gini index to calculate feature importance.
For enhanced transparency and interpretability of the developed models, we intend to employ SHAP values (Shapley Additive exPlanations).SHAP values will provide insights into the workings of different machine learning models, facilitating a deeper understanding of their predictions and aiding healthcare practitioners in making more informed treatment decisions 23 .

Definition of target population
In Taiwan, patients with CKD benefit from a comprehensive multidisciplinary care program known as the pre-ESRD P4P program, aimed at enhancing the quality of their healthcare 9 .A significant majority of our CKD patients are actively enrolled in this program.This initiative offers a holistic approach to patient care, with involvement from a diverse group of healthcare professionals 24 .Patients under this program receive thorough assessments and educational support from this dynamic learning healthcare system 9,25 .Notably, our institute stands as one of the prominent healthcare facilities in Taiwan with the highest number of pre-ESRD patients enrolled.By November 2018, we had successfully enrolled over 10,000 CKD patients in this program.
In this study, we included patients who were participants in the pre-ESRD P4P program, based on our inhospital cohort data spanning from January 1, 2000, to December 31, 2019.To be eligible for inclusion, patients had to meet specific criteria.They were required to be at least 20 years of age and exhibit the following renal function characteristics: Modification of Diet in Renal Disease (MDRD) eGFR less than 45 ml/min/1.73m 2 , as per the classifications outlined by the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) codes N18.3, N18.4, and N18.5, or the ICD-9 code 585.0.Our patient selection process was visually presented in supplementary figure S2.Given the purely analytical nature of this study, the need for informed consent from patients and their family members was waived.The study protocol received thorough review and approval from the Institutional Review Board at TCVGH, bearing approval number CE20026A.All methods employed in this study adhered to the pertinent guidelines and regulations.
It's worth noting that AST-120 usage in Taiwan comes at a monthly cost of 500 US dollars, and it is not covered by the national health insurance.Consequently, not all CKD patients choose to invest in this medication, despite their CKD status.The prescribed dosage of AST-120 consisted of 2 g per package, and the daily amount was determined based on the severity of CKD.At our institute, patients took 2 g once a day if their eGFR ranged between 30-60 ml/min/1.73m 2 .If the eGFR fell within the range of 15-30 ml/min/1.73m 2 , the prescribed dosage was 2 g taken twice a day.For individuals with more severe CKD (eGFR < 15 ml/min/1.73m 2 ), the recommended dosage was 2 g taken three times a day.A majority of CKD patients at our institute adhered to these recommendations.Given the high level of compliance observed in our Pre-ESRD P4P program, coupled with consistent reminders from our educators for patients who self-fund AST-120, it is reasonable to assume that all AST-120 users exhibited good compliance with the prescribed regimen.
AST-120, an expensive medication funded by patients themselves, was identified by its Anatomical Therapeutic Chemical (ATC) code (A07BA01), as utilized in our institute.Consequently, patients were classified into four groups based on renal death and AST-120 status.The target population consisted of patients who received AST-120 treatment and maintained renal survival for two years, while the non-target population included all other patients.To identify the potential target population, we employed AI algorithms that analyzed detailed medication records (ATC codes) and medical histories (ICD-9 and ICD-10), as illustrated in supplementary data 2.

Definition of variables
In our feature engineering and variable selection process, we considered all available variables and potential predictors, encompassing a wide range of factors.These included demographic data such as age and gender, concurrent medications, epidemiological variables, laboratory biomarkers, and comorbidity information.Epidemiological variables covered age (years old), gender, body weight (kg), and body height (cm).Laboratory biomarkers included serum creatinine (mg/dl), eGFR (ml/min/1.73m 2 ), daily proteinuria (g/day), urinary albumin creatinine ratio (mg/g), glycated hemoglobin (%), fasting glucose (mg/dl), aspartate aminotransferase (U/L), alanine aminotransferase (U/L), total bilirubin (mg/dl), total cholesterol (mg/dl), high-density lipoprotein (HDL) cholesterol (mg/dl), low-density lipoprotein (LDL) cholesterol (mg/dl), and triglycerides (mg/dl), as well as systolic and diastolic blood pressures (mmHg).Medication history encompassed conditions such as diabetes mellitus, hypertension, hyperlipidemia, gout, congestive heart failure, cerebrovascular disease, cirrhosis, and malignancy.We also collected data related to habits and physical activity, such as walking exercise, brisk walking, running, smoking, and betel nut consumption.Additionally, medication history included the use of erythropoietin, vitamin D, uric acid-lowering agents, angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, beta-blockers, calcium channel blockers, statins, fibrates, and insulin.
For the sake of broader applicability in future studies, we designated certain variables as necessary features, including age (years old), sex, race, eGFR (ml/min/1.73m 2 ), urinary albumin creatinine ratio (mg/g), systolic blood pressure (mmHg), smoking status, diabetes mellitus, and a history of cardiovascular disease (CVD).Other variables were categorized as alternative features, with their inclusion in the deep learning model contingent upon their performance.

The definition of outcome
The primary outcome of this study focused on patient survival, confirmed by tracking the withdrawal of national health insurance cards.We conducted this study using a right-censoring strategy, allowing us to account for patients who were still alive at the time of data analysis.In addition to survival data, we collected renal function indicators, including serum creatinine and eGFR for further analysis.Patients diagnosed with ESRD were identified as individuals who had undergone dialysis for a minimum duration of 3 months, as evidenced by the acquisition of a certificate of catastrophic illness for dialysis.Furthermore, we analyzed additional surrogate outcomes, including the percentage of patients in stage 5 CKD, time to death, time to ESRD, and time to death or ESRD, to gain a comprehensive understanding of the study's outcomes and implications.

Model building processes
Two-thirds of the patients were randomly allocated to the training group, while the remaining one-third were assigned to the validation group.In the training group, we developed a predictive algorithm based on deep learning.Subsequently, we validated the generated algorithm using the participants in the validation group.Finally, we compared the outcomes between the AI learning model and the logistic regression model.The target population was identified within the training group.Recognizing the imbalanced sample sizes between the target and non-target populations, we applied different techniques such as up-sampling or the Synthetic Minority Oversampling Technique (SMOTE) to balance these two population samples.We considered both the Deep Neural Network (DNN) and Extreme Gradient Boosting (XGBoost) for the initial deep learning approaches.
To build the deep learning model, we implemented batch normalization to normalize the selected features, setting them as the input layer.The model consisted of three hidden layers, and the output layer was designed with 46 middle layers→46 middle layers→46 middle layers→ one-dimensional output layer.We employed Scaled Exponential Linear Unit (SELU) units in the middle layers and a hard sigmoid unit in the output layer as activation functions.
To prevent overfitting, we introduced dropout layers between the hidden layers.These dropout layers were applied to the outputs of the preceding layer, with a dropout rate set at 0.3.Detailed protocols for this approach were as previously described in the study 26 .We conducted hyperparameter optimization in the training group, with specific parameters optimized for XGBoost and DNN detailed in supplementary table S4.Following model training, we validated the model to assess its performance.

Model evaluation and comparison
Within the training group, we conducted a comparative analysis of various ROC (Receiver Operating Characteristic) curves, each corresponding to a distinct algorithm.Subsequently, we calculated the AUC (Area Under the ROC Curve) for each algorithm.In addition to the machine learning algorithms, specifically DNN and XGBoost, we compared their outcomes with those derived from the logistic regression model for a comprehensive evaluation of predictive performance.

Statistical analyses
To assess differences in baseline clinical variables between the target and non-target populations, we employed the independent t-test for continuous variables and the Chi-square test for categorical variables.To mitigate potential baseline differences between AST-120 users and non-AST-120 users, we applied propensity score matching.Furthermore, we compared the AUCs using the Chi-square test.
The classifier model was developed in the training group.In the validation group, we compared renal and patient survival curves between the target and non-target populations using the Kaplan-Meier method and the Cox-proportional hazard model.We ensured the validity of the Cox's proportional hazards model by testing the assumption with Scaled Schoenfeld residuals, which were plotted against time (as shown for HospiceReferral in supplementary figure S1), and no violation of the assumption was observed.
All statistical analyses were conducted using SAS for Windows (version 9.4; SAS, Cary, NC).The deep learning algorithms and other machine learning procedures were performed using Keras, TensorFlow 1.10.0, and Python 3.6.5.

Study approval
This study protocol was reviewed and approved by Institutional Review Board in TCVGH, approval number: CE20026A.all methods were performed in accordance with the relevant guidelines and regulations.

Consent to participate
The study has been granted an exemption from requiring written informed consent, which was approved by Institutional Review Board in TCVGH (approval number : CE20026A.Chih-Chien Lin, MD, MPH is the Chair, Institutional Review Board (I) in TCVGH and he made the above decision.

Revised patient selection for cox model and machine learning analysis
In the initial cohort, a total of 2584 patients were considered.However, we excluded 455 patients due to incomplete questionnaires and medication data, 25 patients due to incomplete laboratory data, and 53 patients due to missing mortality data.As a result, a final dataset comprising 2051 patients was established for the machine learning training dataset (refer to supplementary figure S2).The median follow-up duration was 4.98 years, and the mean follow-up duration was 5.50 years.
Regarding outcomes, AST-120 users exhibited lower mortality (13.51 vs. 37.88%, p < 0.0001) and a lower prevalence of ESRD (44.16 vs. 53.17%,p = 0.0005).Detailed baseline characteristics among the four groups based on AST-120 users or non-AST-120 users, and their relationship with mortality, can be found in supplementary table S1.Following propensity score matching (1:1 matching in supplementary table S2 and 1 S3), no significant differences were observed in all variables between AST-120 users and non-AST-120 users, except for all-cause mortality and the need for dialysis.
The results of univariate and multivariate analyses for all-cause mortality are summarized in Table 2.In the univariate analysis, AST-120 usage was associated with a significantly lower all-cause mortality (HR 0.395, 95% CI 0.295-0.522).In the multivariate analysis, AST-120 usage remained significantly associated with lower all-cause mortality (HR 0.41, 95% CI 0.188-0.896).Additionally, age was associated with a higher all-cause mortality (HR 1.055, 95% CI 1.028-1.083).Even after propensity matching, the HRs remained consistently low: 0.444 after 1:1 matching (supplementary table S2), 0.435 after 1:2 matching (supplementary table S3), 0.436 after 1:3 matching, and 0.451 after 1:4 matching.Regarding accuracy, specificity, and positive predictive value, the best-performing model was XGBoost (with values of 0.72, 0.90, and 0.48, respectively).In terms of sensitivity and negative predictive value, logistic regression had the highest values (at 0.63 and 0.84, respectively).The AUC values were 0.73 for logistic regression, 0.73 for DNN, and 0.69 for XGBoost, indicating similar predictive powers for the decision of whether to use AST-120 or not.

Discussion
AST-120 usage was associated with a reduced incidence of ESRD (44.16% vs. 53.71%,p = 0.0005) and lower mortality (13.51% vs. 37.88%, p < 0.0001).The use of AST-120 was independently linked to a significant reduction in all-cause mortality, with a 59% risk reduction.This effect remained consistent even after adjusting for factors such as age, gender, CKD stage, urinary albumin creatinine ratio, eGFR, gout, hyperlipidemia, diabetes mellitus, smoking, erythropoietin, ARB or ACEi use, calcium channel blocker, and statin use.
These findings suggest that the impact of AST-120 on a patient's survival operates through mechanisms beyond atherosclerosis or metabolic syndrome.Previous studies have reported the association between serum IS levels and patient mortality.In a smaller-scale study involving 150 participants, the highest IS tertile was associated with significantly higher all-cause and cardiovascular mortality (p = 0.001 and 0.012, respectively) 13 .The predictive power of IS for all-cause mortality was independent of age, gender, diabetes mellitus, albumin, hemoglobin, phosphate, and aortic calcification 13 .In another study with 147 subjects, IS levels were associated with major adverse cardiovascular events, with an AUC of 0.708.These findings suggest that IS may play a critical role in predicting cardiovascular disease in CKD patients 27 .A meta-analysis involving 1572 CKD patients, which included 10 prospective and one cross-sectional study 28 found that PC concentration was significantly associated with all-cause mortality (pooled odds ratio 1.16, 95% confidence interval 1.03 to 1.30, p = 0.013).Elevated IS levels were also significantly associated with a higher risk of all-cause mortality (pooled odds ratio 1.10, 95% confidence interval 1.03 to 1.17, p = 0.003).Elevated PC levels were significantly associated with a higher risk of cardiovascular disease (pooled odds ratio 1.28, 95% confidence interval 1.10 to 1.50, p = 0.002) 28 .In summary, serum IS and PC levels have been linked to poorer survival in CKD patients.Notably, the previous observational studies and the meta-analysis had limited case numbers, such as the meta-analysis with only 1572 patients.Moreover, they did not observe a direct association between the intervention involving IS and PC (the use of AST-120) and mortality, unlike the relationship between serum IS/PC levels and mortality.Our present study, with a larger number of cases (1584 CKD patients), has established a direct association between AST-120 usage and patient mortality.The lack of consensus regarding the association between AST-120 and all-cause mortality can be attributed to inadequate dosing and poor medication compliance.AST-120 has been shown to reduce serum IS levels in a dose-dependent manner, and the effect on patient survival is also linked to the dosage of AST-120 29 .A posthoc subgroup analysis of randomized controlled trials conducted in the USA 30 revealed a significant difference between treatment groups in achieving the primary endpoint (HR 0.74; 95%CI 0.56-0.97) in the population with a medication compliance rate of ≥ 67%.However, drug adherence to AST-120 is generally as low as 70% in Japan 31 .Efforts have been made to enhance AST-120's compliance, such as changing its formulation into fine granules that quickly disintegrate with a small amount of water without spreading inside the mouth.This new formulation of AST-120 is expected to improve the ease of taking the medication and promote better adherence to treatment.Comparatively, AST-120 tablets have demonstrated good palatability and can increase medication adherence when compared to AST-120 fine granules, with 70% of patients preferring the switch to tablets 32 .CVD plays a significant role in the overall mortality of CKD patients, and CKD is often considered as being equivalent to coronary heart disease 33 .As a result, the impact of AST-120 on all-cause mortality primarily stems from its ability to reduce CVD.However, the exact mechanism by which AST-120, through the reduction of IS and PC, affects a patient's mortality remains not fully understood.IS is known to enhance the hypermethylation of Klotho, which can contribute to vascular calcification in CKD 34 .In hypertensive rat models, IS has been shown to promote aortic calcification by inducing the expression of osteoblast-specific proteins 35 .It also promotes cell senescence along with aortic calcification and the expression of senescence-related proteins 36 .Exposure to IS and PC has been found to activate inflammation and coagulation signaling pathways in the aorta, which are causally implicated in toxin-induced arterial calcification 37 .Moreover, a retrospective analysis involving 199 CKD patients revealed that the aortic calcification index was significantly lower in patients who took AST-120 [12.2% (2.5-30.3%) vs. 25.7%(13.4-45.3%),p < 0.001] 38 .
Deep learning, a machine learning approach inspired by the functioning of the human brain, is characterized by the combination of layered artificial neurons 39 .It has shown great promise in various clinical scenarios, especially when there is an abundance of data but limited expertise in the specific domain.However, in our study, we encountered challenges in developing a more effective model to predict the impact of AST-120 on mortality in CKD patients (with an AUC of 0.73 in DNN and logistic regression, and 0.69 in XGBoost).Several factors could explain these outcomes.First, deep learning algorithms require extensive training datasets, and their advantage lies in the volume of data available 40 .When provided with sufficient data, deep learning models tend to outperform shallow neural networks, traditional machine learning methods, and basic statistical analyses.In our study, the number of AST-120 users was limited to 385, while non-AST-120 users numbered 2199.These data volumes fell below the threshold necessary to showcase the superiority of DNN.Second, the dataset used for deep learning should ideally be comprehensive, unbiased, and of high quality.Moreover, a longer follow-up period is needed to generate new data that could enhance model performance.Third, it is possible that our AI models lacked some key features.Important variables may have been missing from the study, such as information about protein diet (including the ratio of animal protein intake and adherence to low or very low protein diets), the specific dosage of AST-120, patients' compliance with AST-120, and details about the causes of CKD and other acute kidney injuries.The inclusion of additional variables or critical features could lead to more accurate predictions, but to leverage the full potential of deep learning, larger datasets would be required.Deep learning models with larger architectures are especially data-intensive and tend to perform better with an expanded feature set.Our study may have suffered from a shortage of relevant variables for analysis, which could have limited the model's performance.
Our study is subject to several limitations.Firstly, we did not document the specific dosage of AST-120 administered to patients, although the majority of CKD patients at our institution typically follow recommendations of 2 g/day in stage 3 CKD, 4 g/day in stage 4 CKD, and 6 g/day in stage 5 CKD.Secondly, we lacked data on the adherence of patients to AST-120.However, it's worth noting that patients in pre-ESRD P4P care programs generally display good compliance, and they receive consistent reminders from our educators, particularly those who self-pay for AST-120.Additionally, we did not have data on serum IS and PC levels to confirm the impact of AST-120 on these specific biomarkers.Thirdly, our study did not include detailed information on the specific causes of mortality.Fourthly, due to our study design, we cannot establish a causal relationship between AST-120 and all-cause mortality.In future studies, we plan to collect data from a larger sample of patients, including information on the specific dosage, compliance, and duration of AST-120 usage.Fifthly, there is the presence of selection bias, as more affluent individuals are more likely to afford and access AST-120 treatment.Moreover, physicians who prescribe AST-120 for their patients may be more experienced or have a more proactive approach to therapy.These factors cannot be addressed through propensity score matching.Lastly, it's important to note that our study is retrospective and non-randomized, which implies that there may still be unknown confounding factors that influence our results.

Conclusion
In this cohort study involving CKD patients, AST-120 usage was associated with a decrease in patient mortality.Nevertheless, our artificial intelligence model, as implemented with the existing architecture and algorithm using our dataset, did not demonstrate superior performance when compared to traditional statistical analysis in predicting the decision to prescribe AST-120 or not.

Figure 2 .
Figure 2. Algorithm for patient's selection for AI analysis.

Table 1 .
Baseline characteristics according to the usage of AST-120 or not for statistical analysis.Significant values are in bold.

Table 2 .
UnivariateFigure3displays the ROC curves for DNN, XGBoost, and logistic regression tests.In the training group, statistical significance (p < 0.05) was observed when comparing the AUCs in DNN, logistic regression, and XGBoost.However, in the test group, there were no significant differences in AUCs among DNN, logistic regression, and XGBoost.
, multivariate analysis, and post-propensity matching analysis for all-cause mortality.Significant values are in bold.

Table 3 .
Baseline characteristics for AI analysis according to train and validation group.Significant values are in bold.